Proposing a hybrid approach for emotion classification using audio and video data

Emotion recognition has been a research topic in the field of Human-Computer Interaction (HCI) during recent years. Computers have become an inseparable part of human life. Users need human-like interaction to better communicate with computers. Many researchers have become interested in emotion recognition and classification using different sources. A hybrid approach of audio and text has been recently introduced. All such approaches have been done to raise the accuracy and appropriateness of emotion classification. In this study, a hybrid approach of audio and video has been applied for emotion recognition. The innovation of this approach is selecting the characteristics of audio and video and their features as a unique specification for classification. In this research, the SVM method has been used for classifying the data in the SAVEE database. The experimental results show the maximum classification accuracy for audio data is 91.63% while by applying the hybrid approach the accuracy achieved is 99.26%

The World of Mystery and Crime: Agatha Christie Techniques

And Then There Were None and A Murder is Announced are two prominent works written by the “Queen of Crime” Agatha Christie. While both novels belong to the genre of the murder mystery and detective fiction, the writer employs different literary techniques to build suspense and keep the readers’ engagement until the final scene. Moreover, Agatha Christie also pays great attention to the details of the crime. Providing the audience with certain clues, the writer succeeds to manipulate the reader’s thoughts. Thereby, And Then There Were None and A Murder is Announced are remarkable examples of the murder mystery that is achieved by different literary means making the stories topical literary works.&nbsp

Study the Anticancer Effect of Lepidium sativum Leaves Extract on Squamous Cell Carcinoma (CAL-27) Cell Lines

Leaf aqueous extracts of Lepidium sativum was investigated for anticancer activity on human tongue squamous carcinoma (CAL-27). The results showed that the plant extract inhibit the growth of CAL-27 cells in a dose-dependent manner (70, 100, and 150 µg/ml respectively). The toxic effect of L. sativum extract cause significantly (p < 0.05 and p < 0.01) damage to DNA and increase up the percentage of apoptotic nuclei to reach (30% and 60%) at concentrations of (100 and 150 µg/ml, respectively). Our results also showed that the L. sativum generate reactive oxygen species (ROS) in the mitochondria of CAL-27 cells compared to untreated control. The aqueous extract of the leaves of L. sativum holds great promise for the development of effective drugs for oral cancer treatment strategies. Keywords: Lepidium sativum, Anticancer activities, Medicinal plants, Cell line

Do arbitrators have a duty to report corruption? Maybe . . . Maybe Not

This paper explores whether arbitrators’ have a duty to report corruption or not. It is divided into two parts. In part one, the paper presents the legal status of arbitrators by introducing the contractual, judicial and hybrid theories. Also, it examines the national laws of the US, Egypt, and Saudi Arabia to establish whether there is a legal duty to report or not. In the second part, the paper defines both confidentiality and public policy, and shows where the conflict between the two exists. On the one hand, arbitrators have a duty to protect confidentiality. On the other hand, arbitrators have a duty to insure the award will be enforced and that the contract does not contradict public policy. With this in mind, the paper will present five cases where the competent governments knew about the corruption, yet they did not prosecute the perpetrators. The paper concludes with the contention that arbitrators have no legal duty to report corruption

A New Algorithm for Monte Carlo for American Options

2000 Mathematics Subject Classification: 91B28, 65C05.We consider the valuation of American options using Monte Carlo simulation, and propose a new technique which involves approximating the optimal exercise boundary. Our method involves splitting the boundary into a linear term and a Fourier series and using stochastic optimization in the form of a relaxation method to calculate the coefficients in the series. The cost function used is the expected value of the option using the the current estimate of the location of the boundary. We present some sample results and compare our results to other methods

The Perceptions of Healthcare Students toward the Evidence-Based Practice of Asthma Management

The Perceptions of Healthcare Students toward the Evidence-Based Practice of Asthma Management By Fatimah Alobaidi, BS (Under the Direction of Dr. Rachel Culbreth) BACKGROUND: Despite the growing research work regarding asthma perceptions among different populations, healthcare professional students’ perceptions have not previously been examined. Therefore, it is important to assess healthcare students\u27 perceptions towards the evidence-based practice of asthma management to address the need for designing a targeted intervention to improve understanding of evidence-based management of asthma in college settings. PURPOSE: The aim of the study was to evaluate the Healthcare students’ perceptions Towards the evidence-based practice of asthma management. METHODS: Data were collected through self-administered survey. RESULTS: Sixty students (N=60) were surveyed from three majors: nursing students accounted for 78.3%; followed by respiratory therapy students 15%; and nutrition students 6.7%. The majority of respondents were female (88.3%), while only seven were male (11.7%). 26.7% of the participants self-declared that they had been diagnosed with asthma. Almost half of the sample had no experience in healththerapy (53.3%). Only three participants (5%) were often treating asthma patients while more than half of the participants had never treated asthma patients (68.3%). The findings revealed that healthcare students reported the strongest agreement on the importance of recognizing the signs and symptoms of asthma with a total mean score of 6.85 (SD±.404). Students who had clinical experience demonstrated significantly greater understanding of asthma treatment than those who had no clinical experience (p=.044). The study showed that students who never treated asthma patients had significantly lower knowledge about the causes of asthma (p=.039), signs and symptoms of asthma (p=.004), and the treatment of asthma (p=.005). Asthmatic students rated their knowledge about the signs and symptoms, and treatment of asthma significantly higher than non-asthmatic students (p=.005, p=.014, respectively). CONCLUSIONS: Healthcare students have positive perceptions toward the evidence-based practice of asthma management. Further research with larger sample size, various healthcare professions, and different educational institutions is recommended

Dependability analysis and recovery support for smart grids

The increasing scale and complexity of power grids exacerbate concerns about failure propagation. A single contingency, such as outage of a transmission line due to overload or weather-related damage, can cause cascading failures that manifest as blackouts. One objective of smart grids is to reduce the likelihood of cascading failure through the use of power electronics devices that can prevent, isolate, and mitigate the effects of faults. Given that these devices are themselves prone to failure, we seek to quantify the effects of their use on dependability attributes of smart grid. This thesis articulates analytical methods for analyzing two dependability attributes - reliability and survivability - and proposes a recovery strategy that limits service degradation. Reliability captures the probability of system-level failure; Survivability describes degraded operation in the presence of a fault. System condition and service capacity are selected as measures of degradation. Both reliability and survivability are evaluated using N-1 contingency analysis. Importance analysis is used to determine a recovery strategy that maintains the highest survivability in the course of the recovery process. The proposed methods are illustrated by application to the IEEE 9-bus test system, a simple model system that allows for clear articulation of the process. Simulation is used to capture the effect of faults in both physical components of the power grid and the cyber infrastructure that differentiates it as a smart grid --Abstract, page iii

Distribution Network Planning and Operation With Autonomous Agents

With the restructured power system, different system operators and private investors are responsible for operating and maintaining the electricity networks. Moreover, with incentives for a clean environment and reducing the reliance on fossil fuel generation, future distribution networks adopt a considerable penetration of renewable energy sources. However, the uncertainty of renewable energy sources poses operational challenges in distribution networks. This thesis addresses the planning and operation of the distribution network with autonomous agents under uncertainty. First, a decentralized energy management system for unbalanced networked microgrids is developed. The energy management schemes in microgrids enhance the utilization of renewable energy resources and improve the reliability and resilience measures in distribution networks. While microgrids operate autonomously, the coordination among microgrid and distribution network operators contributes to the improvement in the economics and reliability of serving the demand. Therefore, a decentralized energy management framework for the networked microgrids is proposed. Furthermore, the unbalanced operation of the distribution network and microgrids, as well as the uncertainty in the operating modes of the microgrids, renewable energy resources, and demand, are addressed. The second research work presents a stochastic expansion planning framework to determine the installation time, location, and capacity of battery energy storage systems in the distribution network with considerable penetration of photovoltaic generation and data centers. The presented framework aims to minimize the capital cost of the battery energy storage and the operation cost of the distribution network while ensuring the security of energy supply for the data centers that serve end-users in the data network as well as the reliability requirements of the distribution network. The third research work proposes a coordinated expansion planning of natural gas-fired distributed generation in the power distribution and natural gas networks considering demand response. The problem is formulated as a distributionally robust optimization problem in which the uncertainties in the photovoltaic power generation, electricity load, demand bids, and natural gas demand are considered. The Wasserstein distance metric is employed to quantify the distance between the probability distribution functions. The last research work proposes a decentralized operation of the distribution network and hydrogen refueling stations equipped with hydrogen storage, electrolyzers, and fuel cells to serve hydrogen and electric vehicles. The uncertainties in the electricity demands, PV generation, hydrogen supply, and hydrogen demands are captured, and the problem is formulated as a Wasserstein distance-based distributionally robust optimization problem. The proposed framework coordinates the dispatch of the distributed generation in the distribution network with the hydrogen storage, electrolyzer, and fuel cell dispatch considering the worst-case probability distribution of the uncertain parameters. The proposed frameworks limit the information shared among these autonomous operators using Benders decomposition